Abstract

The efficiency of Monte Carlo methods, commonly used to render participating
media, is directly linked to the manner in which random sampling
decisions are made during path construction. Notably, path construction is
influenced by scattering direction and distance sampling, Russian roulette,
and splitting strategies. We present a consistent suite of volumetric path
construction techniques where all these sampling decisions are guided by
a cached estimate of the adjoint transport solution. The proposed strategy is
based on the theory of zero-variance path sampling schemes, accounting
for the spatial and directional variation in volumetric transport. Our key
technical contribution, enabling the use of this approach in the context of
volume light transport, is a novel guiding strategy for sampling the particle
collision distance proportionally to the product of transmittance and the
adjoint transport solution (e.g., in-scattered radiance). Furthermore, scattering
directions are likewise sampled according to the product of the phase
function and the incident radiance estimate. Combined with guided Russian
roulette and splitting strategies tailored to volumes, we demonstrate
about an order-of-magnitude error reduction compared to standard unidirectional
methods. Consequently, our approach can render scenes otherwise
intractable for such methods, while still retaining their simplicity
(compared to, e.g., bidirectional methods).

Links and Downloads

Acknowledgments

We want to thank the following persons and institutions for providing
some of the models and scenes used in this work: Stanford
3D scanning repository (Buddha), Alvaro Luna Bautista and Joel
Andersdon (Natural History), Bruce Walter (Bumpy Sphere),
and Infinite Realities (InfiniteScan Head). An additional thanks
goes to Robert Hildebrandt for lighting some of the scenes.